ICT andICT and statistics as useful tools forfor the the ... design-Minne_Caspeele-Gent.pdf · MIX...

MIXDESIGNMIXDESIGN

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ICT andICT and statisticsstatistics asas usefuluseful toolstoolsICT and ICT and statisticsstatistics as as usefuluseful tools tools forfor the the optimisationoptimisation of the of the productionproduction processprocess ofof concreteconcreteproductionproduction processprocess of of concreteconcrete

Peter Minne Robby Caspeele

Concrete Innovation Forum - February 14th, 2010

Statisticscan becan bedifficult…

R b thi f li ?Remember this feeling ?

MIXDESIGNMIXDESIGN However some objectionsMIX

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However… some objections

Objection 2Objection 1

Objection 3

“Do not worry about your difficulties in Mathematics.I can assure you mine are still greater.”

4Albert EinsteinRobby Caspeele

MIXDESIGNMIXDESIGN ContentMIX

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Content

1. Introductionusing statistics in concrete production… using statistics in concrete production

2. Water demand and consistency prediction modelsthe building stones for computational concrete design… the building stones for computational concrete design

3. The “Mix design, Mix proportioning” software… a multifunctional ICT tool

4. Some properties of raw material and their variation… input for statistical simulation models

5. Case study… influence of variation of concrete properties on the water demand

6 Conclusions6. Conclusions5

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Content

MIXDESIGNMIXDESIGN Process steering approachMIX

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Process steering approach

effecteffect

MIXDESIGNMIXDESIGN Process steering approachMIX

PROPORTIONINGMIX

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Process steering approach

Examples of statistical tools for quality control

MIXDESIGNMIXDESIGN Probabilistic design and MIX

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PROPORTIONINGevaluation of conformity criteria

Unsafe regionAOQL = 5%AOQL = 5%

EN 206-1

Numerical analyses

Monte Carlo simulations

MIXDESIGNMIXDESIGN Bayesian statistics …MIX

PROPORTIONINGMIX

PROPORTIONING… using all available information

Bayesian non-linearregression for updating

Updating strengthdistributions based on

t t lt regression for updating strength prediction models

test results

Assessment of in-situcharacteristic concrete

strength Influence of conformitycontrol on concrete

propertiesp p

Influence of conformitycontrol on the safety level

of concrete structures11

of concrete structures

MIXDESIGNMIXDESIGN Design of concrete strengthMIX

PROPORTIONINGMIX

PROPORTIONINGprediction models

non-linear regression

MIXDESIGNMIXDESIGN Updating concrete strengthMIX

PROPORTIONINGMIX

MIXDESIGNMIXDESIGN Updating concrete strengthMIX

PROPORTIONINGMIX

MIXDESIGNMIXDESIGN Quality control chartsMIX

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Quality control charts

PROPORTIONINGMIX

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Concrete production at a concrete plant

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Actions should be takenin order to avoid non-conformities

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NON-CONFORMITY !

MIXDESIGNMIXDESIGN Monte Carlo simulationsMIX

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Random numbers: realizations of U(0,1)

“pseudo-random numbers” ripseudo random numbers ri

How to calculate realizations x from X with FX(x)?

Xi xFrorrFx iXiiXi xFrorrFx

MIXDESIGNMIXDESIGN Monte Carlo simulationsMIX

PROPORTIONINGMIX

PROPORTIONING

i bl 1

MODELvariable 1

variable n

RESPONSE

Water demand

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Content

2. Water demand and consistency prediction models… the building stones for computational concrete design… the building stones for computational concrete design

MIXDESIGNMIXDESIGN Water demand and consistencyMIX

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Modelling of solids and voids: 1 grain

3D model: cubes

Volume solids:

Volume voids: 3X

Voids ratio: 3

PROPORTIONINGMIX

Modelling of solids and voids: 2 grains

X"D +mD

m, z spatial parameters

X0"D0+mD1

X"D0+mD1

(1+z)D0

Volume of voids of fine grains increases

Volume of voids of course grains increasesdue to the introduction of fine grains

(loosening effect)

gdue to the introduction of course

(wall effect)

"UU 23"

00 UU (loosening effect) 11 UU

PROPORTIONINGMIX

- In the range U0M :

)1( UnUU

Power’s diagramU1

- In the range MU1 :

)1( 00 UnUUn

0.6 U0 1nUUn

0.4 )1( 10 UUM

UUUUUM

)1( 0UX O

Fine fraction n

0.500.1

X0.30.2 0.4 0.80.6 0.7 1.00.9

)1( 10 UU

PROPORTIONINGMIX

Power’s diagram – Dewar’s real mix

U0 U1 UnU0U0’’

25U1U1’’

PROPORTIONINGMIX

Power’s diagram – Dewar’s real mix

size ratior=0.080

1.1 U0"

U 0 6 U

0.8F +

Fine fraction n

0.500.1

X0.30.2 0.4 0.80.6 0.7 1.00.9 26

PROPORTIONINGMIX

1D3.0 1D75.0 1D3 1D5.7

PROPORTIONINGMIX

1.0Influence of ratio of average grain size r

= 0.80U r=1size ratio

= 0.80

io U 0.6

r=0.2grains course of size grain av.

grains fine of size grain av.r

r=0.04Vo

0.3r=0.01

r ↑0.2

0.70.5 0.60.40.2 0.30.1

Fine fraction n

1.00.90.8

PROPORTIONINGMIX

cement 1+2 sand 1+2 gravel 1+2

+ cement 3 + sand 3 + gravel 3

cement + sand voids skeleton

PROPORTIONINGMIX

Modelling the consistency

Reference slump = 50 mm

The difference in water demand for other slump values ≈ independent of raw material and concrete parameters Empirical function for the

difference in water demand Fs

501 SLF 506

SLSLFs

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Content

MIXDESIGNMIXDESIGN Mix design, mix proportioning:MIX

PROPORTIONINGMIX

PROPORTIONINGa multifunctional ICT tool

PROPORTIONINGMIX

G d t ffGrondstoffen

Management of raw materials

GrondstoffenGrondstoffenCementCement Fijn GranulaatFijn Granulaat Grof GranulaatGrof GranulaatToevoegselToevoegsel HulpstofHulpstof

Parameters

Korrelkromme Abosolute volumieke massa Waterbehoefte bij standaard consistentie

Cementtypes

Portlandtypes Portlandcomposietcement Hoogovencement

Parameters

Volumieke massa Droge materie Chloride gehalte

Parameters Korrelkromme Korrelvolumieke massa Schijnbare volumieke massa Waterbehoefte bij standaard consistentie

Blaine waarde Beta-p-waarde Cementsterkte (1, 2, 7, 28 – dagen) Natrium-equivalent Chloride gehalte k-waarde

Berekende grootheden

Hoogovencement Samengesteld cement

Toevoegsels

Vliegas Kalksteen

Chloride gehalte Natrium-equivalent

Waterabsorptie Natrium-equivalent Chloride gehalte Deeltjes < 63 µm

Berekende grootheden Gemiddelde korrelafmeting Holle Ruimten Ratio S ifi k l k

Plastificeerder S l tifi de e e de g oo ede

Gemiddelde korrelafmeting Holle Ruimten Ratio

Specifiek oppervlak Specifiek oppervlak Day Fijnheidsmodulus

Superplastificeerder Luchtbelvormer

PROPORTIONINGMIX

Concrete specifications according to NBN EN 206-1 and NBN B 15-001

PROPORTIONINGMIX

V ki l tV erw erk in g g ran ula ten- Last analysis

Processing of raw materials

V erw erk in g g ran ula tenV erw erk in g g ran ula ten- Random analysis

- Mean of analyses

- Mean over a time period

- Previous calculated mean

Descriptive statistics of variables

PROPORTIONINGMIX

G ranulatensam enstellingG ranulatensam enstelling Design of inert skeleton

Design of grain size distribution

- target curves

- Power’s diagram and Dewar real mixesDewar real mixes

PROPORTIONINGMIX

C t t th di ti d l

Concrete strength prediction models

- Feret

- Bolomey

Abrams- Abrams

- Dutron

- Dewar- Dewar

- Buist

- HankeHanke

PROPORTIONINGMIX

Mix DesignMix Design“Ontwerp betonmengsel”

PROPORTIONINGMIX

Mix ProportioningMix ProportioningProduction of actual concrete mixes

Variatie van

grondstoffen

Variatie in

de productie

Variatie van

grondstoffen

Variatie in

de productie

Mix Proportioning

Vochtgehalte grondstoffen

Mix Proportioning

Vochtgehalte grondstoffen

Mix ProportioningAanpassing recuperatiewaterMix ProportioningAanpassing recuperatiewater

Beoordeling ten opzichte vanbestaande Mix DesignBeoordeling ten opzichte vanbestaande Mix Design

PROPORTIONINGMIX

Updating concrete strength prediction models

PROPORTIONINGMIX

Simulation of water demand and concrete strength

PROPORTIONINGMIX

CusumCusumQuality control of concrete productionQuality control of concrete production

PROPORTIONINGMIX

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Content

MIXDESIGNMIXDESIGN Some properties of raw materialsMIX

PROPORTIONINGMIX

PROPORTIONINGand their variation

- The raw materials which are used for concrete have intrisic properties and fabrication characteristicsfabrication characteristics

- All these properties and characteristics are subjected to large variations

- Only limited attention is paid to these important variations

- Most often only the concrete strength is systematically predicted and os o e o y e co c e e s e g s sys e a ca y p ed c ed a dmonitored in time

- The water demand and consistency are most often not predicted, althoughy p gthese are significant variables influencing many concrete properties

PROPORTIONINGMIX

sand (fine sand 0/2) - grain size distributions

l i ti80

analyses in time

0.1 1 10

d [ mm]

0.1 1 10

d [mm]

MIXDESIGNMIXDESIGN Some properties of raw materials

DOORVAL fijn zand 0/2fijn zand_0/2_fractie 0.125-0.250

MIXPROPORTIONING

and their variation

fijn zand_0/2_fractie 0.250-0.500

fijn zand_0/2_fractie 0.125-0.500 analyses in time of ‘passing through’

11-01-08 20-04-08 29-07-08 6-11-08 14-02-09 25-05-09 2-09-09 11-12-09 21-03-10

PROPORTIONINGMIX

analyses of grain size distribution

Histogram fractie 0.125 -0.250mm Histogram fractie 0.250 -0.500mm

7 5 5 5 5 5 5 5 r

Doorval [%]

PROPORTIONINGMIX

sand (fine sand 0/2) - grain size distributions Log (mean size of the size fraction)=0.5(log(upper size)+log(lower size))

)fractionsizetheofsizemeanlog(xpropn.vol)sizemean(Log

analyses of derived properties: mean size

mean size = 0.3145 mm

00.1 1 10

d [ mm]

PROPORTIONINGMIX

sand (fine sand 0/2) - grain size distributions Distribution of mean size

Monte Carlo simulations of mean size

[mm]gemiddelde 0.3099

gst. Dev 0.0187

10Freq

3039 615

0.29 0.3

0.31 0.3

0.34 0.3

0.36 M

Mean size [mm]

PROPORTIONINGMIX

analyses of derived properties: volumetric mass and voids ratio

i d it ( k t )• grain density (pyknometer)

• bulk density (recipient)

• voids ratio

PROPORTIONINGMIX

sand (fine sand 0/2) - grain size distributions Distribution of voids ratio

Monte Carlo simulations of voids ratio

[-]gemiddelde 1.1201

10121416

st. Dev 0.0801

9475 125

Voids ratio [-]51

PROPORTIONINGMIX

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Content

MIXDESIGNMIXDESIGN Influence of variation of concrete MIX

PROPORTIONINGMIX

PROPORTIONINGproperties on the water demand

i bl 1 100

140Monte Carlo simulations water demand

MODELvariable 1

variable n

RESPONSE

Water demand

PROPORTIONINGMIX

Concrete recipe

Mix Design [kg/m³]CEM III/A 42.5 LA 260

Fly ash 15Fly ash 15Water 170

Sand 0/2 504Sand 0/4 393Sand 0/4 393

Coarse Aggregates 6/20 954Water reducer (Sky) 1.62

PROPORTIONINGMIX

Statistical characteristics of raw materials(based on an observation period of 1 year)

M i V id ti

(based on an observation period of 1 year)

Constituents Mean size mean [mm] (st.dev.)

Voids ratiomean [-] (st.dev.)

CEM III/A 42.5 LA 0.01202 (0.00068) 0.8643 (0.0126) Fly ash 0 01949 (0 00105) 0 6787 (0 0247)Fly ash 0.01949 (0.00105) 0.6787 (0.0247)

Sand 0/2 0.3114 (0.0205) 1.1463 (0.0822) Sand 0/4 1.0259 (0.1275) 0.7794 (0.1114)

Coarse Aggregates 14.0771 (1.3259) 0.9183 (0.0605)Coarse Aggregates 14.0771 (1.3259) 0.9183 (0.0605)

PROPORTIONINGMIX

Random simulation of the water demand

Simulation Water demand Simulation mean [kg] (st.dev.) Properties of all constituents are varying 169.8 (9.29) Only properties of the binder are varying 170.4 (1.18)

O l ti f d i 173 9 (5 81)

140Only properties of sand are varying 173.9 (5.81)

Only properties of coarse aggregates are varying 171.1 (7.29)

y Concrete strength results estimation of the actual variation in water demand

40 Water demand

mean [kg] (st. dev.) Actual variation of the water 173.5 (11.52)

Water demand

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100100100100100100

properties on the water demand

grof_zand_onder60

grof_zand_onderRandom simulation of

grof_zand_boven

grof_tussen

grof_zand_boven

grof_tussen

grof_zand_boven

grof_tussen

grof_zand_boven

grof_tussen

grof_zand_boven

grof_tussen

grof_zand_boven

grof_tussen

Random simulation of grain size distribution

00.01 0.1 1 10

grof_zand_onder

grof_zand_boven

grof_tussen

grof_zand_boven

grof_tussen

grof_zand_boven

grof_tussen

grof_zand_boven

grof_tussen

grof_zand_boven

grof_tussen

grof_zand_boven

grof_tussen

0.01 0.1 1 100

0.01 0.1 1 10

00.01 0.1 1 10

PROPORTIONINGMIX

good coarse_sand_u

good coarse zand a

iv good coarse_zand_a

good coarse_sand_b

sand EN12620

sand NBN B11-011_u

00.01 0.1 1 10

sand NBN B11-011_a

sand NBN B11-011_b

diameter (mm)

Simulation Water demand

mean [kg] (st.dev.) Sand according to NBN 163.69 (8.78)g ( )

Sand according to EN 12620 184.9 (10.97) “Good” concrete sand 166.5 (4.23)

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Content

MIXDESIGNMIXDESIGN ConclusionsMIX

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Conclusions

• The simulated water demand are comparable with the water demand obtained by the strength results diagrams of Powers and the theory of the particle mixtures of Dewar g y p

provide a useful method for estimating the water demand

• The magnitude of the variation in water demand is of the same magnitude as the variation in the actual water content the variability of the properties of the raw materials is the main origin

of the variability in water demand

• More stringent specifications are required for the acceptable boundaries of the grain size distribution according to the standard

• The models can be used to predict the water demand for new mix designs and to predict the water demand when the raw materials parameters are changed

changed

Thank you for your attention !Thank you for your attention !Thank you for your attention !Thank you for your attention !

MIXMIXMIX

DESIGNMIX

DESIGN

MIXPROPORTIONING

Dr. ir. Robby CaspeeleIng. Peter Minne

Technologiepark-Zwijnaarde 9049052 Zwijnaarde

Robby.Caspeele@UGent.be

Gebroeders Desmetstraat 19000 GentPeter.Minne@kahosl.be

ICT andICT and statistics as useful tools forfor the the ... design-Minne_Caspeele-Gent.pdf · MIX...

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